The unexpected dynamics of COVID-19 in Manaus, Brazil: Was herd immunity achieved?
Preprint
- 19 February 2021
- preprint
- research article
- Published by Cold Spring Harbor Laboratory
Abstract
In late March 2020, SARS-CoV-2 arrived in Manaus, Brazil, and rapidly developed into a large-scale epidemic that collapsed the local health system, and resulted in extreme death rates. Several key studies reported that ∼76% of residents of Manaus were infected (attack rate AR≃76%) by October 2020, suggesting protective herd immunity had been reached. Despite this, in November an unexpected second wave of COVID-19 struck again, and proved to be larger than the first creating a catastrophe for the unprepared population. It has been suggested that this could only be possible if the second wave was driven by reinfections. Here we use novel methods to model the epidemic from mortality data, evaluate the impact of interventions, in order to provide an alternative explanation as to why the second wave appeared. The method fits a “flexible” reproductive number R0(t) that changes over the epidemic, and found AR≃30-34% by October 2020, for the first wave, which is far less than required for herd immunity, yet in-line with recent seroprevalence estimates. The two-strain model provides an accurate fit to observed epidemic datasets, and finds AR≃70% by March 2021. Using genomic data, the model estimates transmissibility of the new P.1 virus lineage, as 1.9 times as transmissible as the non-P1. The model thus provides a reasonable explanation for the two-wave dynamics in Manaus, without the need to rely on reinfections which until now have only been found in small numbers in recent surveillance efforts.Significance: This paper explores the concept of herd immunity and approaches for assessing attack rate during the explosive outbreak of COVID-19 in the city of Manaus, Brazil. The event has been repeatedly used to exemplify the epidemiological dynamics of the disease and the phenomenon of herd immunity, as claimed to be achieved by the end of the first wave in October 2020. A novel modelling approach reconstructs these events, specifically in the presence of interventions. The analysis finds herd immunity was far from being attained, and thus a second wave was readily possible, as tragically occurred in reality. Based on genomic data, the multi-strain model gives insights on the new highly transmissible variant of concern P.1 and role of reinfection.Keywords
This publication has 37 references indexed in Scilit:
- Characterizing the reproduction number of epidemics with early subexponential growth dynamicsJournal of The Royal Society Interface, 2016
- Estimating the Reproduction Number of Ebola Virus (EBOV) During the 2014 Outbreak in West AfricaPLoS Currents, 2014
- Explaining rapid reinfections in multiple-wave influenza outbreaks: Tristan da Cunha 1971 epidemic as a case studyProceedings. Biological sciences, 2011
- Mechanistic modelling of the three waves of the 1918 influenza pandemicTheoretical Ecology, 2011
- Plug-and-play inference for disease dynamics: measles in large and small populations as a case studyJournal of The Royal Society Interface, 2009
- Inapparent infections and cholera dynamicsNature, 2008
- Inference for nonlinear dynamical systemsProceedings of the National Academy of Sciences of the United States of America, 2006
- How generation intervals shape the relationship between growth rates and reproductive numbersProceedings. Biological sciences, 2006
- A note on generation times in epidemic modelsMathematical Biosciences, 2006
- A Simple Model for Complex Dynamical Transitions in EpidemicsScience, 2000